Calculating Unique Social Networking System Users Performing An Action On A Social Networking System Object

ABSTRACT

A social networking system generates one or more metrics describing user interactions with objects to describe the popularity of the objects among users. Data describing actions performed by social networking system users on objects stored by the social networking system to identify an action, the user performing the action and the object on which the action was performed. Stored actions performed on a selected object are retrieved and a subset of the actions is generated by sampling the stored actions at a sampling rate. A number of unique users included in the subset is determined and used along with the sampling rate to determine the number of unique users included in the retrieved actions, which is used to derive a metric describing user interaction with the selected object.

BACKGROUND

This disclosure relates generally to metrics for determining popularity,and more specifically to metrics for determining popularity based onuser actions.

Social networking systems allow their users to easily interact andcommunicate with each other. A variety of different types of socialnetworking systems exist that provide mechanisms allowing users tointeract within their social networks. In this context, a user may be anindividual or any other entity, such as a business or other non-personentity. Social networking systems also allow their users to interactwith each other, or with other objects maintained in a social networkingsystem, by performing various actions. For example, users may postcomments to pages associated with other users, view images, view video,listen to audio data or perform other actions on various objectsmaintained by the social networking system.

Often, social networking users may desire to identify objects maintainedby the social networking system in which multiple users are interested.For example, a user may seek to identify pages that a number of otherusers have interacted with over a day, a week or another recent timeinterval. This allows the user to identify and view content in whichusers have recently taken an interest.

While conventional social networking systems allow users to expresspreferences for objects, a user typically provides this information onceper object, making it difficult to discern whether users are recentlyinterested in an object or had previously been interested in an object.For example, if a song was popular years ago, many users may havepreviously expressed a preference for a page associated with the song,but the song and its associated page are not necessarily currentlypopular. Similarly, determining a number of actions performed on anobject provides limited information about the popularity of the page, asa small number of users may be responsible for a large number of actionsperformed on an object. For example, a small number of users may performa large number of actions on a page associated with an obscure hobby.

SUMMARY

A social networking system includes user profiles associated with itsusers and maintains objects on which the users perform actions. Examplesof objects include a page maintained by the social networking system,content posted to a page maintained by the social networking system, astatus update from a user, a photograph, a video, an audio file, a link,an application, a check-in at a location, or any other contentmaintained by the social networking system. The social networking systemalso stores action data describing actions performed by users and theobjects on which the actions were performed. For example, stored actiondata describing an action includes a user identifier of the userperforming the action, a description of the action and an objectidentifier of the object on which the action was performed. Based on thestored action data, the social networking system calculates one or moremetrics describing user interactions with objects. One or more of themetrics may describe the popularity of an object based on the number ofunique social networking system users performing at least one action onthe object (e.g., by posting content that refers to the object).

To calculate the one or more metrics for an object, the socialnetworking system retrieves stored action data associated with an objectidentifier corresponding to the object. The retrieved action datadescribes actions performed by social networking system that areassociated with the object identifier. To efficiently calculate the oneor more metrics, the retrieved action data associated with the objectidentifier is sampled at a sampling rate. This reduces the amount ofaction data associated with the object to reduce the time forcalculating a metric. The sampling rate may depend on the size of theretrieved action data, so that fewer samples of the retrieved actiondata are included in the subset as the number of actions described bythe retrieved action data increase. A number of unique user identifiersincluded in the subset of the action data is determined and used, alongwith the sampling rate, to determine a number of unique user identifiersin the retrieved action data associated with the object identifier. Oneor more metrics may then be determined from the number of unique useridentifiers in the retrieved action data associated with the objectidentifier, such as a total number of unique user identifiers, a numberof unique user identifiers within a specified time range, a total numberof unique user identifiers associated with a specified type of action orany other suitable metric. The one or more metrics are stored andassociated with the object identifier, allowing them to be readilyretrieved.

In addition to generating one or more metrics associated with objects,the social networking system may calculate one or more metricsdescribing user interactions with topics. Multiple objects maintained bythe social networking system may be associated with the same topic, so ametric describing user interactions with topics accounts for actionsperformed by users on various objects associated with a topic. Forexample, the social networking system determines object identifiersassociated with a topic and retrieves action data associated with theobject identifiers. The action data is sampled at a sampling rate, asdescribed above, to generate a subset of the action data and the numberof unique user identifiers in the subset of the action data isdetermined. From the sampling rate and the number of unique useridentifiers in the subset of the action data, the number of unique useridentifiers in the action data is determined and used to generate one ormore metrics describing user interaction with the topic.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system architecture for providing contentitems to users of a social networking system, in accordance with anembodiment of the invention.

FIG. 2 is a block diagram of a social networking system, in accordancewith an embodiment of the invention.

FIG. 3 is a flow chart of a method for determining a popularity metric,in accordance with an embodiment of the invention.

The Figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION System Architecture

FIG. 1 is a high level block diagram illustrating a system environment100 of a social networking system 140. The system environment 100comprises one or more client devices 110, a network 120, one or morethird-party websites 130 and the social networking system 140. Inalternative configurations, different and/or additional components maybe included in the system environment 100. The embodiments describedherein can be adapted to online systems that are not social networkingsystems.

The client devices 110 comprise one or more computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 140. For example, the client devices 110 may be desktopcomputers, laptop computers, smartphones, personal digital assistants(PDAs) or any other device including computing functionality and datacommunication capabilities. A client device 110 is configured tocommunicate via the network 120, which may comprise any combination oflocal area and/or wide area networks, using both wired and wirelesscommunication systems. In one embodiment, a client device 110 executesan application allowing a user of the client device 110 to interact withthe social networking system 140. For example, a client device 110executes a browser application to enable interaction between the clientdevice 110 and the social networking system 140 via the network 120.

In some embodiments, a client device 110 also has location determinationcapabilities for determining a geographic location where the clientdevice 110 is located. For example, a client device 110 includes aglobal positioning system (GPS) or other suitable system for determininga location for the client device 110. The client device 110 maycommunicate its location information to the social networking system 140or to a third-party website 130 subject to privacy settings specified bya user of the client device 110. The privacy settings allow a user tolimit distribution of the client device location to entities identifiedby the user or allow a user to specify a subset the client devicelocations (e.g., locations that the user approves for communicating toan entity) to other entities via the network 120.

The client devices 110 are configured to communicate with each other,with the third-party website 130 and/or with the social networkingsystem 140 via the network 120, which may comprise any combination oflocal area and/or wide area networks, using both wired and wirelesscommunication systems. In one embodiment, the network 120 uses standardcommunications technologies and/or protocols. Thus, the network 120 mayinclude links using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, CDMA, digitalsubscriber line (DSL), etc. Similarly, the networking protocols used onthe network 220 may include multiprotocol label switching (MPLS),transmission control protocol/Internet protocol (TCP/IP), User DatagramProtocol (UDP), hypertext transport protocol (HTTP), simple mailtransfer protocol (SMTP) and file transfer protocol (FTP). Dataexchanged over the network 120 may be represented using technologiesand/or formats including hypertext markup language (HTML) or extensiblemarkup language (XML). In addition, all or some of links can beencrypted using conventional encryption technologies such as securesockets layer (SSL), transport layer security (TLS), and InternetProtocol security (IPsec). The third party website 130, or multiplethird-party websites 130, may be coupled to the network 120 forcommunicating with the social networking system 140, which is furtherdescribed below in conjunction with FIG. 2.

FIG. 2 is a block diagram of an embodiment of a social networking system140. In the embodiment shown by FIG. 2, the social networking system 140includes a user profile store 205, a content store 210, an action logger215, an action log 220, an edge store 225, a metric calculation engine230, a metric store 235, a log sampler 240, a web server 245, and atopic extraction engine 250. In other embodiments, the social networkingsystem 140 may include additional, fewer, or different modules forvarious applications. Conventional components such as networkinterfaces, security functions, load balancers, failover servers,management and network operations consoles, and the like are not shownso as to not obscure the details of the system architecture.

Each user of the social networking system 140 is associated with a userprofile, which is stored in the user profile store 205. A user profileincludes declarative information about the user that was explicitlyshared by the user, and may also include profile information inferred bythe social networking system 140. In one embodiment, a user profileincludes multiple data fields, each data field describing one or moreattributes of the corresponding user of the social networking system140. The user profile information stored in user profile store 205describes the users of the social networking system 140, includingbiographic, demographic, and other types of descriptive information,such as work experience, educational history, gender, hobbies orpreferences, location and the like. A user profile may also store otherinformation provided by the user, for example, images or videos. Incertain embodiments, images of users may be tagged with identificationinformation of users of the social networking system 140 displayed in animage. A user profile in the user profile store 205 may also maintainreferences to actions by the corresponding user performed on objects inthe object store 210 and stored in the action log 220.

The content store 210 stores content objects. A content object, or“object,” may include any type of object on the social networking system140, such as a page post, a status update, a photo, a video, a link, ashared content item, a gaming application achievement, a check-in eventat a local business and so on. Content objects include objects createdby users of the social networking system 140, such as status updatesthat may be associated with photo objects, location objects, and otherusers, photos tagged by users to be associated with other objects in thesocial networking system 140, such as events, pages, and other users,and applications installed on the social networking system 140. In someembodiments, the content objects are received from third-partyapplications or third-party applications separate from the socialnetworking system 140. Content “items” represent single pieces ofcontent that are represented as objects in the social networking system130. In this way, users of the social networking system 130 areencouraged to communicate with each other by posting text and contentitems of various types of media through various communication channels,increasing the interaction of users with each other and increasing thefrequency with which users interact within the social networking system140.

In one embodiment, the social networking system 140 includes a topicextraction engine 250, which identifies one or more topics associatedwith objects in the content store 210. To identify topics associatedwith content items, the topic extraction engine 150 may identify anchorterms described in the content items (e.g., in posts of the user)associated with the action and determines the meaning of the terms asfurther described in U.S. application Ser. No. 13/167,701, filed Jun.24, 2011, which is hereby incorporated by reference in its entirety. Forexample, the topic extraction engine 250 determines one or more topicsassociated with a page maintained in the content store 210 on which auser performed an action. The one or more topics associated with anobject are stored and associated with an object identifier correspondingto the object. In various embodiments, associations between objectidentifiers and topics are stored in the topic extraction engine 250 orin the content store 210. This allows retrieval of one or more topicsassociated with an object identifier or retrieval of object identifiersassociated with a specified topic.

The action logger 215 receives communications about user actions onand/or off the social networking system 140. The action logger 215populates the action log 220 with information about user actions,allowing the actions to be tracked. Example actions include: adding aconnection to the other user, sending a message to the other user,uploading an image, reading a message from the other user, viewingcontent associated with the other user, attending an event posted byanother user, among others. In addition, a number of actions describedin connection with other objects are directed at particular users, sothese actions are associated with those users as well. These actions arestored in the action log 220. The action logger 215 also receives datadescribing interaction between a user and a sequence and communicatesthe interaction between user and sequence to the action log 220.

The action log 220 may be used by the social networking system 140 totrack users' actions on the social networking system 140 as well asexternal websites that communicate information back to the socialnetworking system 140, such as the third party website 130. Users mayinteract with various objects on the social networking system 140,including commenting on posts, sharing links, and checking-in tophysical locations via a mobile device, accessing content items in asequence or other interactions. Information describing these actions isstored in the action log 220. Additional examples of interactions withobjects on the social networking system 140 included in the action log220 include commenting on a photo album, communications between users,becoming a fan of a musician, adding an event to a calendar, joining agroups, becoming a fan of a brand page, creating an event, authorizingan application, using an application and engaging in a transaction.Additionally, the action log 220 records a user's interactions withadvertisements on the social networking system 140, interactions withcontent items, as well as interactions with applications operating onthe social networking system 140.

The action log 220 may also include user actions on external websites,such as third-party website 130. For example, the action logger 215receives data describing a user's interaction with a third-party website130, which is stored in the action log 220. Examples of actions where auser interacts with a third-party website 130 includes a user expressingan interest in a third-party website 130 or another entity, a userposting a comment to the social networking system 140 that discusses athird-party website 120, or a web page 122 within the third-partywebsite 130, a user posting to the social networking system 140 aUniform Resource Locator (URL) or other identifier associated with athird-party website 130, a user attending an event associated with athird-party website 130 or any other action by a user that is related toa third-party website 130. For example, an e-commerce website thatprimarily sells luxury shoes at bargain prices may recognize a user of asocial networking system 140 through social plug-ins that enable thee-commerce website to identify the user of the social networking system.Because users of the social networking system 140 are uniquelyidentifiable, e-commerce websites, such as this luxury shoe reseller,may use the information about these users as they visit their websites.The action log 220 records data about these users, including viewinghistories, advertisements that were clicked on, purchasing activity, andbuying patterns. Hence, interactions between a social networking systemuser and a third-party website 130 may also be stored in the action log220.

When storing an action, the action log 220 stores a unique useridentifier and a unique object identifier for each action. This allowsdata associated with a specific user identifier or a specific objectidentifier to be retrieved from the action log 220. For example, anobject identifier associated with a specific page is used to retrieveactions associated with the object identifier performed by users.Additionally, data in the action log 220 associated with an action mayinclude a date and time when the action was performed, allowing data tobe retrieved from the action log 220 based on a specific date or time orbased on a range of dates and/or times.

The edge store 225 stores the information describing connections betweenusers and other objects on the social networking system 140 in edgeobjects. Some edges may be defined by users, allowing users to specifytheir relationships with other users. For example, users may generateedges with other users that parallel the users' real-life relationships,such as friends, co-workers, partners, and so forth. Other edges aregenerated when users interact with objects in the social networkingsystem 140, such as expressing interest in a page on the socialnetworking system, sharing a link with other social networking systemusers and commenting on posts made by other users of the socialnetworking system. The edge store 225 stores edge objects that includeinformation about the edge, such as affinity scores for objects,interests, and other users. Affinity scores may be computed by thesocial networking system 140 over time to approximate a user's affinityfor an object, interest, and other users in the social networking system140 based on the actions performed by the user. Example embodiments ofcomputing affinity scores are described in a related application,“Contextually Relevant Affinity Prediction in a Social NetworkingSystem,” U.S. patent application Ser. No. 12/978,265, filed on Dec. 23,2010, which is hereby incorporated by reference in its entirety.Multiple interactions between a user and a specific object may be storedin one edge object in the edge store 225, in one embodiment. In someembodiments, connections between users and objects may be stored in thecontent store 210 with connections between users stored in the userprofile store 205, or the content store 210.

Based on data in the action log 220, the metric calculation engine 230calculates one or more metrics describing user actions performed on aspecified object. The metric calculation engine 230 retrieves data fromthe action log 220 associated with a specified object identifierassociated with the specified object and determines one or more metricsdescribing user interaction with the specified object. The dataretrieved from the action log 220 may be further refined by action type.The metric calculation engine 230 may retrieve data from the action log220 identifying actions performed by users on the specified object thatare distributed to additional users. For example, the social networkingsystem 140 identifies certain types of actions performed by a user toother users connected to the user, so the metric calculation engine 230retrieves actions performed on a specified object having the certaintypes.

The metric calculation engine 230 determines a number of unique useridentifiers included in the retrieved data and calculates one or moremetrics based on the number of unique user identifiers. For example, themetric may provide a count of the number of unique user identifiers ormay provide a count of the number of unique user identifiers within aspecified time interval. As an additional example, the metric mayidentify a number of unique users performing a specified type of actionon the specified object (e.g., a number of unique users sharing a pageor a number of unique users commenting on a page).

The metric calculation engine 230 may also be used to calculate one ormore metrics describing user actions associated with a topic. Asdescribed above, the topic extraction engine 250 identifies one or moretopics associated with objects, allowing associations between objectidentifiers and topics to be maintained. A topic may be identified tothe metric calculation engine 230, which retrieves object identifiersassociated with the topic from the topic extraction engine 250 and/orfrom the content store 210. Using the object identifiers associated withthe topic, the metric calculation engine 230 retrieves action data fromthe action log 210 associated with the object identifiers. The retrievedaction data is then sampled to generate a subset and the number ofunique user identifiers associated with the retrieved action data isdetermined from the subset and a sampling rate. This may be used tocalculate one or more topic-specific metric, allowing assessment of userinteractions with topics that may be associated with multiple objectsrather than interactions with a specific object.

However, the action log 220 may include a large number of actionsassociated with a specified object identifier, which may delay metricgeneration. To reduce the time needed to generate a metric from a largenumber of actions, the metric calculation engine 230 communicates with asampling engine 240, which generates a subset of actions associated witha specified object identifier by capturing samples from the actionsassociated with the specified object identifier at a sampling rate. Forexample, a sampling rate of five indicates that one out of five actionsfrom the actions associated with the object identifier is included inthe subset of actions. The metric calculation engine 230 uses thegenerated subset as well as information about how the data was sampledto more quickly generate the metric. In various embodiments, thesampling rate varies according to the number of actions in the actionlog data or the page action data. For example, the sampling rate may beinversely proportional to the number of actions associated with thespecified identifier.

In one embodiment, the metric calculation engine 230 generates one ormore metrics using a subset of actions associated with a specifiedobject identifier and a sampling rate received from the sampling engine240. For example, the metric calculation engine 230 extrapolates thenumber of unique user identifiers associated with actions associatedwith a specified object identifier by multiplying the sampling rate bythe number of unique user identifiers in the subset of actionsassociated with the specified object identifier. The metric calculationengine 230 may also adjust a metric based on the percentage of uniqueuser identifiers with respect to the number of actions in the sampledaction log data. In other embodiments, the metric calculation engine 230may use any other suitable method for extrapolating the number of uniqueuser identifiers from a subset of actions received from the samplingengine 240. The sampling engine and the metric calculation engine 230are further described below in conjunction with FIG. 3.

The metric store 235 stores metrics generated by the metric calculationengine and associates the stored metrics with their corresponding objectidentifier. Hence, a metric maintained by the metric store 235 isassociated with an object identifier. A stored metric may also beassociated with a timestamp, such as the date and time when the metricwas generated. The metric calculation engine 230 may use timestampsassociated with stored metric when calculating metrics for an objectidentifier. For example, if a metric in the metric store 235 isassociated with a timestamp that is older than a specified time or isassociated with a timestamp for a time greater than a specified intervalfrom a current time, an updated metric is generated by the metriccalculation engine and stored in the metric store 235. Additionally, astored metric associated with an object identifier may be removed fromthe metric store 235 when a more recent metric for the object identifieris received or when the timestamp associated with the stored metric isgreater than a specified interval from the current time. Depending onthe amount of activity associated with an object, the metric for thatobject may be recalculated in near real time (e.g. once or more anhour). In some embodiments, a new metric is calculated for each objectevery time an action occurs that is associated with that object.

The web server 245 links the social networking system 140 via thenetwork 120 to the one or more client devices 110, as well as to the oneor more third party websites 130. The web server 245 serves web pages,as well as other web-related content, such as JAVA®, FLASH®, XML and soforth. The web server 245 may provide the functionality of receiving androuting messages between the social networking system 140 and a clientdevice 110, for example, instant messages, queued messages (e.g.,email), text and SMS (short message service) messages, or messages sentusing any other suitable messaging technique. A user may send a requestto the web server 245 to upload information, for example, images orvideos that are stored in the content store 210. Additionally, the webserver 245 may provide API functionality to send data directly to nativeuser device operating systems, such as iOS®, ANDROID™, webOS® or asimilar embedded operating system.

When a user requests an object from the social networking system 140,the web server 245 receives the request and retrieves a metricassociated with the object's object identifier from the metric store235. If the metric store 235 does not include a metric or if the storedmetrics associated with the object identifier are not current, one ormore metrics are calculated by the metric calculation engine 230. Ametric associated with the object may be included in or otherwisepresented to a user along with the object requested from the web server245. Additionally, users may access one or more metrics associated withan object by identifying a metric and the object to the web server 245,such as through an application programming interface (API). For example,an API may allow a user to retrieve a popularity metric and a creationdate and time for the popularity metric as well as allow the user toinitiate calculation of an updated popularity metric via the metriccalculation engine 230.

Calculating a Metric of User Interaction with Objects in a SocialNetworking System

FIG. 3 shows a flow chart of a method 300 for determining one or moremetrics describing interaction with an object in a social networkingsystem 140 by social networking system users. An object is selected andan object identifier associated with the object is retrieved 310 fromthe content store 210. For example, a web server 245 receives an objectidentifier and a metric calculation engine 230 retrieves 310 the objectidentifier from the web server 245. In some embodiments, a topic isidentified to the metric calculation engine 230 via the web server 245and objects associated with the topic are identified from the topicextraction engine 250 and/or the content store 210. The metriccalculation engine 230 retrieves 310 object identifiers associated witheach of the objects associated with the topic from the content store 210and/or from the topic extraction engine 250.

From the retrieved object identifier, or retrieved object identifiers ifa metric is to be generated for a topic, the metric calculation engine220 retrieves 320 action data from the action log 220 associated withthe retrieve object identifier. In one embodiment, the retrieved actiondata includes actions that are communicated or otherwise distributed toat least one user in addition to the user performing the action. Thisallows the retrieved data to capture actions that reflect exposure of anobject or topic to additional social networking system users, providinga more accurate assessment of its popularity among social networkingsystem users. As described above in conjunction with FIG. 2, the actiondata in the action log associates an object identifier with a performedaction and also associates a user identifier of the user performing theaction with the action. The action data may further associate atimestamp with an action to indicate when the action was performed. Forexample, stored action data may have a format of “(object identifier),(action), (user identifier), (timestamp),” or a similar format. Theretrieved action data includes actions performed on the objectassociated with the retrieved object identifier, or retrieved objectidentifiers.

Additional information may be provided along with the object identifierto specify the metric to be generated. For example, a subset of actionsmay be provided along with an object or object identifier to retrieve320 action data associated with the object identifier and correspondingto an action in the subset of actions. A time interval may also bespecified so that the retrieved action data includes actions associatedwith a timestamp within a specified time interval or associated withtimestamps later than a specified time.

A subset of the action data associated with the retrieved objectidentifier is generated 330 by sampling the retrieved action data at asampling rate. This reduces the number of actions associated with theretrieved object identifier when the retrieved action data includes alarge number of actions, while allowing accurate assessment of userinteraction with the object associated with the retrieved objectidentifier. A sampling engine 240, further described above inconjunction with FIG. 2, determines the sampling rate and generates 330the subset by sampling the retrieved action data using the samplingrate. In some embodiments, the sampling rate varies based on the numberof actions included in the action data, allowing the sampling rate tomore effectively reduce the number of actions included in the subset.Generating the subset of action data allows one or more metrics to betgenerated more efficiently.

The number of unique user identifiers in the subset of action data isdetermined 340 by the metric calculation engine 230. In someembodiments, the number of unique identifiers in the subset isdetermined 340 for one or more specified actions or for a specified timeinterval. Determining the number of unique user identifiers allows themetric calculation engine 230 to more accurately gauge user interest ina selected object or in objects associated with a selected topic. Fromthe number of user identifiers in the subset of action data and thesampling rate, the metric calculation engine 230 determines a number ofunique user identifiers associated with the selected object, or with theselected topic. In one embodiment, the metric calculation engine 230multiplies the number of unique user identifiers associated with thesubset by the sampling rate to determine the number of unique useridentifiers associated with the retrieved action data. For example, ifthe sampling rate is five, one out of five actions in the action data isincluded in the subset of action data, so the number of unique useridentifiers in the subset of action data is multiplied by five todetermine the number of unique user identifiers associated with theretrieved action data.

Based on the number of unique user identifiers and/or on the unique useridentifiers associated with the selected object or selected topic, themetric calculation engine 230 generates 350 one or more metricsassociated with the selected object and/or the selected topic. Forexample, the metric identifies a total number of unique user identifiersassociated with the selected object or with the selected topic, a totalnumber of unique user identifiers associated with the selected object orwith the selected topic during a time interval, a total number of uniqueuser identifiers associated with the selected object and performing aspecified type of action or actions or any other suitable metric.

In one embodiment, the metric calculation engine 230 may generate 350 ametric personalized for a user selecting the object. For example, ametric identifying a total number of unique user identifiers associatedwith users connected to the user selecting the object is generated 350by identifying the unique user identifiers and generating a count ofunique user identifiers having an edge associated with the userselecting the object from the edge store 225 and/or from the userprofile store 205. Hence, the social networking system may present auser viewing a page with a metric identifying the number of uniquesocial networking system users or interacting with the page as well asan additional metric identifying the number of unique users connected tothe user viewing the page that have interacted with the page,personalizing the user interaction information.

The generated metric, or metrics, is associated with the selected objector with the selected topic and stored in the metric store 235 tosimplify subsequent retrieval. A metric associated with an object may beretrieved from the metric store 235 and displayed proximate to theobject by the social networking system, allowing a user to see thepopularity of the object. For example, the social networking system maydisplay “100 like this” or “11,561,530 talking about this” next to arepresentation of a page in the social networking system, allowing auser to account for the page's popularity when deciding to view thepage. Multiple metrics may be displayed, such as “100 like this. 50 ofyour friends like this,” to allow the user to see the popularity of apage, or other object, with users as a whole as well as with other usersconnected to the user. As another example, metrics may be retrieved andused when ranking objects in a search for content in the socialnetworking system. For example, the number of unique users may be usedwhen ordering search results to allow the ordering to account for thepopularity of objects in the search results so that more popular objectshave a higher position in the search results.

SUMMARY

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A computer implemented method comprising: storingaction data in a social networking system, the action data describingactions performed by users of the social networking system, each actionassociated with a user identifier and an object identifier; retrieving aselected object identifier associated with a selected object; retrievingaction data associated with the selected object identifier, theretrieved action data identifying actions performed by users of thesocial networking system in connection with the selected object andcommunicated to at least one additional user; sampling the retrievedaction data at a sampling rate to generate a subset of action dataassociated with the selected object identifier; determining a number ofunique user identifiers associated with the selected object identifierbased on a number of unique user identifiers in the subset of actiondata associated with the selected object identifier and based on thesampling rate; generating a metric associated with the selected objectidentifier based on the number of unique user identifiers associatedwith the selected object identifier; and storing the metric and anassociation between the metric and the selected object identifier. 2.The computer implemented method of claim 1, wherein the selected objectis a page maintained by the social networking system.
 3. The computerimplemented method of claim 1, wherein the selected object is selectedfrom a group consisting of: an application maintained by the socialnetworking system, a video included in the social networking system, apost of content to a page maintained by the social networking system andan image maintained by the social networking system.
 4. The computerimplemented method of claim 1, wherein retrieving action data associatedwith the selected object identifier comprises: retrieving action dataassociated with the selected object identifier and including actionsassociated with one or more specified action types.
 5. The computerimplemented method of claim 1, wherein retrieving action data associatedwith the selected object identifier comprises: retrieving action dataassociated with the selected object identifier and including actionsassociated with times within a specified time range.
 6. The computerimplemented method of claim 1, wherein the sampling rate is based on anumber of actions identified by the retrieved action data associatedwith the selected object identifier.
 7. The computer implemented methodof claim 6, wherein the sampling rate is inversely proportional to thenumber of actions identified by the retrieved action data associatedwith the selected action identifier.
 8. The computer implemented methodof claim 1, wherein determining the number of unique user identifiersassociated with the selected object identifier comprises: determiningthe number of unique user identifiers in the subset of action data; andcalculating the number of unique user identifiers associated with theselected object by multiplying the number of unique user identifiers bythe sampling rate.
 9. The computer implemented method of claim 1,wherein generating the metric associated with the selected objectidentifier comprises: determining a number of unique user identifiersassociated with actions performed within a specified time range.
 10. Thecomputer implemented method of claim 1, wherein generating the metricassociated with the selected object identifier comprises: determining anumber of unique user identifiers associated with actions having one ormore specified types.
 11. The computer implemented method of claim 1,wherein generating the metric associated with the selected objectidentifier comprises: determining a number of unique user identifiersassociated with the selected object identifier and associated with usersconnected to a user accessing the selected object.
 12. A computerimplemented method comprising: storing objects in a social networkingsystem, each object associated with an object identifier and includingcontent and associated with a topic describing the content; storingaction data in the social networking system, the action data describingactions performed by users of the social networking system, each actionassociated with a user identifier and an object identifier; receiving aspecified topic; determining object identifiers associated with theselected topic; retrieving action data associated with the determinedobject identifiers, the retrieved action data identifying actionsperformed by users of the social networking system in connection with anobject associated with the determined object identifiers andcommunicated to at least one additional user; sampling the retrievedaction data at a sampling rate to generate a subset of action dataassociated with the selected object identifiers; determining a number ofunique user identifiers associated with the determined objectidentifiers based on a number of unique user identifiers in the subsetof action data associated with the determined object identifiers andbased on the sampling rate; generating a metric associated with thespecified topic based on the number of unique user identifiersassociated with the determined object identifiers; and storing themetric and an association between the metric and the specified topic.13. The computer implemented method of claim 12, wherein the objectidentifiers associated with the selected topic include at least oneobject identifier associated with a page maintained by the socialnetworking system.
 14. The computer implemented method of claim 12,wherein the object identifiers associated with the selected topic areassociated with one or more selected from a group consisting of: anapplication maintained by the social networking system, a video includedin the social networking system, a post of content to a page maintainedby the social networking system and an image maintained by the socialnetworking system.
 15. The computer implemented method of claim 12,wherein retrieving action data associated with the determined objectidentifiers comprises: retrieving action data associated with each ofthe object identifiers and including actions associated with one or morespecified action types.
 16. The computer implemented method of claim 12,wherein retrieving action data associated with the determined objectidentifier comprises: retrieving action data associated with each of theobject identifiers and including actions associated with times within aspecified time range.
 17. The computer implemented method of claim 12,wherein the sampling rate is based on a number of actions identified bythe retrieved action data associated with the selected objectidentifier.
 18. The computer implemented method of claim 17, wherein thesampling rate is inversely proportional to the number of actionsidentified by the retrieved action data associated with the selectedaction identifier.
 19. The computer implemented method of claim 12,wherein determining the number of unique user identifiers associatedwith the determined object identifiers comprises: determining the numberof unique user identifiers in the subset of action data; and calculatingthe number of unique user identifiers associated with the selectedobject by multiplying the number of unique user identifiers by thesampling rate.
 20. The computer implemented method of claim 12, whereingenerating the metric associated with the specified topic comprises:determining the number of unique user identifiers associated withactions performed within a specified time range.
 21. The computerimplemented method of claim 12, wherein generating the metric associatedwith the selected topic comprises: determining the number of unique useridentifiers associated with actions having one or more specified actiontypes.
 22. The computer implemented method of claim 12, whereingenerating the metric associated with the selected object identifiercomprises: determining a number of unique user identifiers associatedwith the selected object identifier and associated with users connectedto a user accessing the selected object.
 23. A computer implementedmethod comprising: storing action data in a social networking system,the action data describing actions performed by users of the socialnetworking system, each action associated with a user identifier and anobject identifier; retrieving a selected object identifier associatedwith a selected object; retrieving action data associated with theselected object identifier, the retrieved action data identifyingactions performed by users of the social networking system in connectionwith the selected object and communicated to at least one additionaluser; determining a number of unique user identifiers associated withthe selected object identifier in the retrieved action data; generatinga metric associated with the selected object identifier based on thenumber of unique user identifiers associated with the selected objectidentifier; and storing the metric and an association between the metricand the selected object identifier.